import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import time

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)


def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)


def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')


x = tf.placeholder("float", shape=[None, 784], name="input_x")
y_ = tf.placeholder("float", shape=[None, 10])

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1,28,28,1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float", name="keep_prob")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

# 最终的输出
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 对输出进行验证
# 计算交叉熵
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
# Adam 优化参数/模型
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 对模型进行预测
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
# 转换并输出预测结果
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))


sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())


print("=====================begin training======================")
pick = time.time()
# 使用训练数据集对模型进行训练
for i in range(20000):
  batch = mnist.train.next_batch(50)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:batch[0], y_: batch[1], keep_prob: 1.0})
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("Total time ", time.time() - pick)
print("======================end training======================")

# 使用测试数据集对模型进行评估
testBatch = mnist.test.next_batch(3000)
res = accuracy.eval(feed_dict={x: testBatch[0], y_: testBatch[1], keep_prob: 1.0})
print("test accuracy", res)

# 保存训练模型
# saver = tf.train.Saver()
# tf.add_to_collection('y_conv', y_conv)
# saver.save(sess, './save/model')

